An increasing number of distributed applications process continuously flowing data from geographically distributed sources, perform analytics on the streamed data, and provide analysis results to entities that may also be geographically distributed. The continuously flowing data may be generated from sensor measurements that capture real-time data describing current operating characteristics of a remote device. The sensor measurements may derive from multiple different types of sensors installed at various locations (e.g., brakes, engine, steering mechanism, cooling system, passenger ventilation, power source, etc.) on a currently-moving vehicle, aircraft or watercraft, for example.
Event stream processing (ESP) can be used to analyze and understand millions of events per second, while detecting patterns of interest as they occur in real time. While processing with sub-millisecond response times for high-volume throughput, data streams can be assessed with ESP to derive insights and take appropriate actions. In streaming analytics, data is received and processed in real-time, which poses many difficulties in designing effective machine learning algorithms.